TensorFlow中关于tf.app.flags命令行参数解析模块

#coding:utf-8 import tensorflow as tf FLAGS = tf.app.flags.FLAGS # tf.app.flags.DEFINE_string("param_name", "default_val", "description") tf.app.flags.DEFINE_string("train_data_path", "/home/feige", "training data dir") tf.app.flags.DEFINE_string("log_dir", "./logs", " the log dir") tf.app.flags.DEFINE_integer("train_batch_size", 128, "batch size of train data") tf.app.flags.DEFINE_integer("test_batch_size", 64, "batch size of test data") tf.app.flags.DEFINE_float("learning_rate", 0.001, "learning rate") def main(unused_argv): train_data_path = FLAGS.train_data_path print("train_data_path", train_data_path) train_batch_size = FLAGS.train_batch_size print("train_batch_size", train_batch_size) test_batch_size = FLAGS.test_batch_size print("test_batch_size", test_batch_size) size_sum = tf.add(train_batch_size, test_batch_size) with tf.Session() as sess: sum_result = sess.run(size_sum) print("sum_result", sum_result) # 使用这种方式保证了，如果此文件被其他文件 import的时候，不会执行main 函数 if __name__ == '__main__': tf.app.run()   # 解析命令行参数，调用main 函数 main(sys.argv)

def run(main=None, argv=None): """Runs the program with an optional 'main' function and 'argv' list.""" f = flags.FLAGS # Extract the args from the optional `argv` list. args = argv[1:] if argv else None # Parse the known flags from that list, or from the command # line otherwise. # pylint: disable=protected-access flags_passthrough = f._parse_flags(args=args) # pylint: enable=protected-access main = main or sys.modules['__main__'].main # Call the main function, passing through any arguments # to the final program. sys.exit(main(sys.argv[:1] + flags_passthrough))